Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments. In a smart radio environment, surfaces are capable of manipulating the propagation of incident electromagnetic waves in a programmable manner to actively alter the channel realization, which turns the wireless channel into a controllable system block that can be optimized to improve overall system performance. In this article, we provide a tutorial overview of reconfigurable intelligent surfaces (RIS) for wireless communications. We describe the working principles of reconfigurable intelligent surfaces (RIS) and elaborate on different candidate implementations using metasurfaces and reflectarrays. We discuss the channel models suitable for both implementations and examine the feasibility of obtaining accurate channel estimates. Furthermore, we discuss the aspects that differentiate RIS optimization from precoding for traditional MIMO arrays highlighting both the arising challenges and the potential opportunities associated with this emerging technology. Finally, we present numerical results to illustrate the power of an RIS in shaping the key properties of a MIMO channel.

The accelerated move towards the adoption of the industrial Internet of Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the ” False Data Injection” (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this study, we present a novel method of FDI attack detection using Autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs. Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM) based approach used for the same purpose. The denoising AE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data. M. Aboelwafa, K. Seddik, and Y. Gadallah are with the Electronics and Communications Engineering Department at the American University in Cairo, New Cairo, 11835 Egypt (e-mail: mariam.aboelwafa@aucegypt.edu, kseddik@aucegypt.edu, ygadallah@ieee.org). M. Eldefrawy is with the School of Information Technology, Halmstad University, 301 18 Halmstad, Sweden (e-mail: mohamed.eldefrawy@hh.se). M. Gidlund is with the Department of Information Systems and Technology, Mid Sweden University, 851 70 Sundsvall, Sweden (e-mail: mikael.gidlund@miun.se) Parts of this work was funded by STINT under grant IB2018-7469 Copyright (c) 2020 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org.

In this letter, a novel approach for outage probability analysis of the multinode amplify-and-forward relay network is provided. It is shown that the harmonic mean of two exponential random variables can be approximated, at high signal-to-noise ratio (SNR), to be an exponential random variable. The single relay case considered before is a special case of our analysis. Based on that approximation, an outage probability bound is derived which proves to be tight at high SNR. Based on the derived outage probability bound, optimal power allocation is studied. Simulation results show a performance improvement, in terms of symbol error rate, of the optimal power allocation compared to the equal power-allocation scheme

We present a study of using game theory for protecting wireless sensor networks (WSNs) from selfish behavior or malicious nodes. Due to scalability, low complexity and disseminated nature of WSNs, malicious attacks can be modeled effectively using game theory. In this study, we survey the different game-theoretic defense strategies for WSNs. We present a taxonomy of the game theory approaches based on the nature of the attack, whether it is caused by an external attacker or it is the result of an internal node acting selfishly or maliciously. We also present a general trust model using game theory for decision making. We, finally, identify the significant role of evolutionary games for WSNs security against intelligent attacks; then, we list several prospect applications of game theory to enhance the data trustworthiness and node cooperation in different WSNs.

In recent years, ambient backscatter communications have gained a lot of interests as a promising enabling technology for Internet-of-Things and green communications. In ambient backscatter communication systems, ultra-low power devices are able to transmit information by backscattering ambient RF signals generated by legacy communication systems, such as Wi-Fi and cellular networks. This paper is concerned with ambient backscatter communications over legacy OFDM signals. We propose a backscatter modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. The proposed modulation scheme allows both binary and higherorder modulation using noncoherent energy detection. We investigate the detector design and analyze the error performance of the proposed scheme. We provide an exact expression for the error probability for the binary case, whereas accurate approximate expressions for the error probability are derived for the M-ary case. We corroborate our analysis using Monte- Carlo simulation and investigate the effects of varying the OFDM symbol size, maximum channel delay spread, and the number of receive antennas on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in the literature for backscatter communication over ambient OFDM signals in different scenarios.

In this paper, we investigate the use of reconfigurable intelligent surfaces (RIS) to allow multiple user pairs to communicate simultaneously over the same channel. We propose a Riemannian manifold optimization approach to solve the problem of configuring the RIS passive reflection coefficients to minimize the total interference under constant modulus constraints. We compare the proposed approach to the widely-used semidefinite relaxation approach (SDR) for dealing with the constant modulus constraints. We investigate, using extensive numerical simulations, the effects of various system parameters, such as the number of users, the number of RIS elements, and the fraction of power received through the RIS. Our results demonstrate that RISs can substantially minimize interference allowing multiple user pairs to simultaneously communicate over the same channel and that the proposed approach vastly outperforms the semidefinite relaxation-based approach, which fails to find satisfactory solutions.